To solve the problem that PointNet++, a direct point cloud data processing deep neural network, cannot thoroughly learn the shape information of point cloud, and SAFE-PointNet++ (spatial awareness and feature enhancement PointNet++), a 3D point cloud classification and segmentation method is proposed, which combines both spatial awareness module and feature enhancement module (SAFE). Firstly, the spatial awareness (SA) module is designed to help the feature extraction network integrate the weight information of spatial structure when the feature dimension is raised, thus enhancing the expression function of the feature in space. Secondly, the feature enhancement (FE) module is designed so that the additional information of the point cloud can be fully used by respectively splitting and encoding the enhanced geometric information and additional information. The experiment results show that SAFE-PointNet++ achieves higher classification and segmentation accuracy than the other ten classical networks on ModelNet40 and S3DIS datasets.